Credit Scorecards Research Articles

Credit Cost Management and Financial Performance of Hotel Businesses in Kenya

This study explores the impact of credit cost management on the financial performance of businesses in Kenya's hospitality sector. Specifically, it examines how interest rates, loan collateral, and loan repayment terms influence the profitability and financial stability of small and medium-sized enterprises (SMEs) in the industry. The study is supported by Loanable funds theory, Credit scorecards theory and Tradeoff theory. Through a desk review of existing literature, the study identifies that high interest rates increase the cost of borrowing, creating financial pressure on businesses, while lower rates support profitability. Loan collateral is found to be a major determinant of access to credit, with businesses that provide sufficient collateral securing loans more easily. Flexible loan repayment terms contribute to better cash flow management and liquidity, improving overall financial performance. The study concludes that effective credit cost management is critical for the success of hospitality businesses and recommends that financial institutions and policymakers collaborate to provide more favorable credit terms, including lower interest rates, reduced collateral requirements, and more flexible repayment options. Increased transparency in loan agreements and government interventions to support SMEs are also essential for fostering financial growth in the sector.

Character Counts: Psychometric-Based Credit Scoring for Underbanked Consumers

Psychometric-based credit scores measure important personality traits that are characteristic of good borrowers’ behaviors. While such data can potentially improve credit models for underbanked consumers, the utility of psychometric data in consumer lending is still largely understudied. The present study contributes to the literature in this respect, as it is one of the first studies to evaluate the efficacy of psychometric-based credit scores for predicting future loan defaults among underbanked consumers. The results from two culturally diverse samples of loan applicants (Sub-Saharan Africa, n = 1113; Western Europe, n = 1033) found that psychometric scores correlated significantly with future loan defaults (Gini = 0.28–0.31) and were incrementally valid above and beyond the banks’ own credit scorecards. These results highlight the theoretical basis for personality in financial behaviors, as well as the practical utility that psychometric scores can have for credit decisioning in general and the facilitation of financial inclusion for underbanked consumer groups in particular.

Credit Scorecards & Forecasting Default Events – A Novel Story of Non-financial Listed Companies in Pakistan

Credit Scorecards & Forecasting Default Events – A Novel Story of Non-financial Listed Companies in Pakistan

A novel framework for enhancing transparency in credit scoring: Leveraging Shapley values for interpretable credit scorecards.

Credit scorecards are essential tools for banks to assess the creditworthiness of loan applicants. While advanced machine learning models like XGBoost and random forest often outperform traditional logistic regression in predictive accuracy, their lack of interpretability hinders their adoption in practice. This study bridges the gap between research and practice by developing a novel framework for constructing interpretable credit scorecards using Shapley values. We apply this framework to two credit datasets, discretizing numerical variables and utilizing one-hot encoding to facilitate model development. Shapley values are then employed to derive credit scores for each predictor variable group in XGBoost, random forest, LightGBM, and CatBoost models. Our results demonstrate that this approach yields credit scorecards with interpretability comparable to logistic regression while maintaining superior predictive accuracy. This framework offers a practical and effective solution for credit practitioners seeking to leverage the power of advanced models without sacrificing transparency and regulatory compliance.

Optimization of credit scorecard combinations based on quantum annealing algorithm

With the development of the times, the usage of loan-related business is increasing. However, most of the credit score card portfolio methods used nowadays are manually designed and supplemented with computer calculations, so the bank yields are low. Therefore, it is of great interest to study the application of quantum computers in credit score card portfolio optimization for better rating of customer credit ratings. Based on the given credit score card data, a linear programming model is developed. Firstly, the data is processed into two sets of one-dimensional data by Python and the working range of the two sets of data is found. Secondly, after deriving the objective function expressions according to the given conditions, the data were visualized by building a 3D grid matrix with the pass rate and bad debt rate as the XOY plane and the final benefit to the bank as the Z axis. Subsequently, the decision variables in the linear programming model are converted into binary variables, and the original model is transformed into a QUBO model by converting the constraints into binary variables. After analyzing the QUBO model, the quantum annealing method is chosen to solve the optimal solution of the objective function. And based on this, the case of three selected credit score card combinations and any three credit score cards are considered.

A General Architecture for a Trustworthy Creditworthiness-Assessment Platform in the Financial Domain

The financial domain is making huge advancements thanks to the exploitation of artificial intelligence. As an example, the credit-worthiness-assessment task is now strongly based on Machine Learning algorithms that make decisions independently from humans. Several studies showed remarkable improvement in reliability, customer care, and return on investment. Nonetheless, many users remain sceptical since they perceive the whole as only partially transparent. The trust in the system decision, the guarantee of fairness in the decision-making process, the explanation of the reasons behind the decision are just some of the open challenges for this task. Moreover, from the financial institution's perspective, another compelling problem is credit-repayment monitoring. Even here, traditional models (e.g., credit scorecards) and machine learning models can help the financial institution in identifying, at an early stage, customers that will fall into default on payments. The monitoring task is critical for the debt-repayment success of identifying bad debtors or simply users who are momentarily in difficulty. The financial institution can thus prevent possible defaults and, if possible, meet the debtor's needs. In this work, the authors propose an architecture for a Creditworthiness-Assessment duty that can meet the transparency needs of the customers while monitoring the credit-repayment risk. This preliminary study carried out an experimental evaluation of the component devoted to the credit-score computation and monitoring credit repayments. The study shows that the authors’ architecture can be an effective tool to improve current Credit-scoring systems. Combining a static and a subsequent dynamic approach can correct mistakes made in the first phase and foil possible false positives for good creditors.

Some Insights about the Applicability of Logistic Factorisation Machines in Banking

Logistic regression is a very popular binary classification technique in many industries, particularly in the financial service industry. It has been used to build credit scorecards, estimate the probability of default or churn, identify the next best product in marketing, and many more applications. The machine learning literature has recently introduced several alternative techniques, such as deep learning neural networks, random forests, and factorisation machines. While neural networks and random forests form part of the practitioner’s model-building toolkit, factorisation machines are seldom used. In this paper, we investigate the applicability of factorisation machines to some binary classification problems in banking. To stimulate the practical application of factorisation machines, we implement the fitting routines, based on logit loss and maximum likelihood, on commercially available software that is widely used by banks and other large financial services companies. Logit loss is usually used by the machine learning fraternity while maximum likelihood is popular in statistics. Depending on the coding of the target variable, we will show that these methods yield identical parameter estimates. Often, banks are confronted with predicting events that occur with low probability. To deal with this phenomenon, we introduce weights in the above-mentioned loss functions. The accuracy of our fitting algorithms is then studied by means of a simulation study and compared with logistic regression. The separation and prediction performance of factorisation machines are then compared to logistic regression and random forests by means of three case studies covering a recommender system, credit card fraud, and a credit scoring application. We conclude that logistic factorisation machines are worthy competitors of logistic regression in most applications, but with clear advantages in recommender systems applications where the number of predictors typically outnumbers the number of observations.

Secure Withdrawal From Atm Using Biometric Fingerprint

Automated Teller Machines (ATMs) are a very convenient way to withdraw money wherever you are ATMs are in high demand because users rely heavily on their ATMs. But if someone has access to us, they can easily access our bank accounts. The only solution for ATM security is a 6-digit PIN. There are still many vulnerabilities that criminals can use to fraud customer data. To avoid such thievery and fraud, the actual creator embedded in the smart card A biometric identification system, and a database server are integrated. Under biometrics, we've got iris recognition, face recognition, and fingerprint recognition. By using these techniques, your account will be easily accessible and no one will be able to withdraw your funds illegally. This is designed to provide better security for ATMs. It is hoped that this method will minimize ATM fraud and allow customers to use their ATMs more safely. Based on previous implementations and test results, we are accustomed to improving the efficiency of fingerprint ATM scanner feature update speed, and the security framework has passed the test. The cause of this painting is to grow surety and safety and cast off the use of Automated Teller Machine credit score and debit cards.

An Overview on the Landscape of R Packages for Open Source Scorecard Modelling

The credit scoring industry has a long tradition of using statistical models for loan default probability prediction. Since this time methodology has strongly evolved, and most of the current research is dedicated to modern machine learning algorithms which contrasts with common practice in the finance industry where traditional regression models still denote the gold standard. In addition, strong emphasis is put on a preliminary binning of variables. Reasons for this may be not only the regulatory requirement of model comprehensiveness but also the possibility to integrate analysts’ expert knowledge in the modelling process. Although several commercial software companies offer specific solutions for modelling credit scorecards, open-source frameworks for this purpose have been missing for a long time. In recent years, this has changed, and today several R packages for credit scorecard modelling are available. This brings the potential to bridge the gap between academic research and industrial practice. The aim of this paper is to give a structured overview of these packages. It may guide users to select the appropriate functions for the desired purpose. Furthermore, this paper will hopefully contribute to future development activities.

Concept of Peer-to-Peer Lending and Application of Machine Learning in Credit Scoring

Numerous applications of AI are found in the banking sector. Starting from the front-office, enhancing customer recognition and personalized services, continuing in the middle-office with automated fraud-detection systems, ending with the back-office and internal processes automatization. In this paper we provide comprehensive information on the phenomenon of peer-to-peer lending in the modern view of alternative finance and crowdfunding from several perspectives. The aim of this research is to explore the phenomenon of peer-to-peer lending market model. We apply and check the suitability and effectiveness of credit scorecards in the marketplace lending along with determining the appropriate cut-off point. We conducted this research by exploring recent studies and open-source data on marketplace lending. The scorecard development is based on the P2P loans open dataset that contains repayments record along with both hard and soft features of each loan. The quantitative part consists in applying a machine learning algorithm in building a credit scorecard, namely logistic regression.

Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering

The Basel II Accord pointed out benefits of credit risk management through internal models to estimate Probability of Default (PD). Banks use default predictions to estimate the loan applicants’ PD. However, in practice, PD is not useful and banks applied credit scorecards for their decision making process. Also the competitive pressures in lending industry forced banks to use profit scorecards, which show the profitability of customers. Applying these scorecards together makes the loan decision making process for banks more confusing. This paper has an obvious and clean solution for facilitating the confusion of loan decision making process by combining the credit and profit scorecards through introducing a matrix sequential hybrid credit scorecard. The applicability of the introduced matrix sequential hybrid scorecard results are shown using data from an Iranian bank.

Autobin: A predictive approach towards automatic binning using data splitting

The concept of binning is known by many names: discretisation, classing, grouping and quantisation. It entails the mapping of continuous or categorical data into discrete bins. Binning is an important pre-processing step in most predictive models and considered a basic data preparation step in building a credit scorecard. Credit scorecards are mathematical models which attempt to provide a quantitative estimate of the probability that a customer will display a defined behaviour (e.g. default) with respect to their current credit position with a lender. Among the practical advantages of binning are the removal of the effects of outliers and a way to handle missing values. Many binning methods exist but they are often time consuming to actually carry out. We propose a new method, Autobin, that is based on data splitting and maximising a cross-validation form of the predicted log-likelihood. Autobin has the advantage of being nearly automatic and requires very little by way of tuning parameters. In a limited simulation study done, it was found that Autobin outperforms its competitors.

Approaches for credit scorecard calibration: An empirical analysis

Financial institutions use credit scorecards for risk management. A scorecard is a data-driven model for predicting default probabilities. Scorecard assessment concentrates on how well a scorecard discriminates good and bad risk. Whether predicted and observed default probabilities agree (i.e., calibration) is an equally important yet often overlooked dimension of scorecard performance. Surprisingly, no attempt has been made to systematically explore different calibration methods and their implications in credit scoring. The goal of the paper is to integrate previous work on probability calibration, to re-introduce available calibration techniques to the credit scoring community, and to empirically examine the extent to which they improve scorecards. More specifically, using real-world credit scoring data, we first develop scorecards using different classifiers, next apply calibration methods to the classifier predictions, and then measure the degree to which they improve calibration. To evaluate performance, we measure the accuracy of predictions in terms of the Brier Score before and after calibration, and employ repeated measures analysis of variance to test for significant differences between group means. Furthermore, we check calibration using reliability plots and decompose the Brier Score to clarify the origin of performance differences across calibrators. The observed results suggest that post-processing scorecard predictions using a calibrator is beneficial. Calibrators improve scorecard calibration while the discriminatory ability remains unaffected. Generalized additive models are particularly suitable for calibrating classifier predictions.

A Neural Network Approach for Microfinance Credit Scoring

Credit scoring has been successfully applied in domains as mortgage loans and credit scorecards. However, credit scoring with the microfinance industry is a relatively recent application. Most of the financial and banking institutions are using logistic regression to build a credit scorecard. Among the new method artificial neural networks have been applied in various studies of scorecard modelling. This paper analyzes comparative descriptions of both the artificial neural network and logistic regression model in credit scoring for microfinance data. The theoretical features and properties, which include parameters, variable selection, and model evaluation, followed by comparisons of the advantages and disadvantages of both the models are analytically reviewed.

The effect of introducing economic variables into credit scorecards: an example from invoice discounting

We demonstrate how introducing economic variables into a credit scorecard improves the predictive power of that scorecard. Such a scorecard can forecast default rates accurately, even when economic conditions change. This means we can develop a single-step approach to estimate the point-in-time probabilities of default (PDs), which are required by the Basel Accords' banking regulations. A one-step approach has several advantages over the more standard approach, which involves first estimating scores with no economic variables, then segmenting the portfolio by score bands and estimating the PD per segment. To build such a scorecard, we decompose it into the population odds and weights of evidence.We show that economic variables model the dynamics of the population odds part of the scorecard, which leads to an improvement in prediction. We then apply this extension to credit scoring to a real problem in invoice discounting. This is when banks lend to small businesses using the invoices that the businesses have issued as collateral. There is a significant volume of such lending, but it is not often addressed in the literature. The scorecards used to assess the risk of default of such small businesses are very similar to the behavioral scorecards used to assess default risk in lending to consumers. The results show that modeling the population odds by economic variables is very effective, but there is little improvement in the scorecard's performance if we model the dynamics of the weights of evidence by adding interactions between the economic variables and the performance characteristics of the borrowing firm

Default definition selection for credit scoring

In this paper some of the main causes of the recent financial crisis are briefly discussed. Specific attention is paid to the accuracy of credit-scoring models used to assess consumer credit risk. As a result, the optimal default definition selection (ODDS) algorithm is proposed to improve credit-scoring for credit risk assessment. This simple algorithm selects the best default definition for use when building credit scorecards. To assess ODDS, the algorithm was used to select the default definition for the random forest tree algorithm. The resulting classification models were compared to other models built using the unselected default definitions. The results suggest that the models developed using the default definition selected by the ODDS algorithm were statistically superior to the models developed using the unselected default indicators.

Time varying or static cut-offs for credit scorecards

This note points out that the ability of credit scorecards to separate Goods from Bads changes over time. A simple way of dealing with such changes is to adjust the cut-off scores being used. These adjustments can be made by using the score to log odds relationship which is regularly monitored. In a case study there are decreases in the costs—in some case considerable decreases—of using the scorecard by making such adjustments compared with making no adjustments.

Legally scored

PurposeAchieving equal treatment of credit applicants has been a legitimate concern of legislators and the credit industry. However, measures taken to date in attempting to comply with anti‐discrimination laws arguably do not allow for the most effective use of credit scoring models, and could run counter‐intuitive to the intention of legislation through indirect discrimination. The purpose of this paper is to offer an alternative interpretation that preserves the intention of legislation and also retains the integrity and effectiveness of credit scoring models.Design/methodology/approachThe paper makes a legal analysis of anti‐discrimination laws in the UK, with US law as a comparison, aiming to demonstrate that concerns in using information protected under anti‐discrimination laws as variables may be misplaced, because nothing in these laws precludes the inclusion of all relevant variables in modelling.FindingsThe inclusion of variables representing protected characteristics in credit scoring models may not contradict current anti‐discrimination laws.Research limitations/implicationsLimitations exist from the perspectives of customer relationship and the need for further checks and balances. Conclusive validation of the findings will need to come from the courts. The paper provides a springboard for empirical research on whether the inclusion of variables representing protected characteristics in credit scorecards continues to produce better decision‐making models.Practical implicationsThe findings benefit credit risk modelling as a whole in facilitating the development of credit scorecards that are in compliance with anti‐discrimination laws, without sacrificing their effectiveness.Originality/valueThe paper presents a fresh perspective and alternative solution to legal concerns regarding the use of protected characteristics in credit scoring, which will be useful to the credit industry.

Innovation in Corporate Credit Scoring: Z-Score Optimization

This paper applies novel concepts from consumer credit scoring to corporate credit scoring. The literature of bankruptcy scoring and score combination and modeling is examined and financial ratios and combined credit score super scorecards are essentially interaction effects. An optimal credit score is built when these effects are properly specified and all predictive information in variables is extracted using a properly specified model. The fact that optimal credit scorecards can be built using interaction effects is show by superior performance of random forests out of the box as they include interaction effects in the model which are not included in logistic regression and trees unless explicitly specified. To build optimal scorecards one must detect and specify interaction effects of which financial ratios are simply one example or form. Random forests provide an invaluable tool to suggest variable interactions and in building optimal consumer and corporate scorecards.

Improving Logistic Regression/Credit Scorecards Using Random Forests: Applications with Credit Card and Home Equity Datasets

The following paper uses an approach to credit scoring which is based on the premise that credit scoring models should be built on affordability data (income, assets, free cash flows, and cash flow proxies, etc.) and that these variables should have meaningful interactions with other scorecard attributes. The approach comprises of using Random Forests to identify variables with the most predictive power which are then used to permute interaction terms with other variables to build improved scorecards using logistic regression. The problem with most credit scorecards is that they use logistic regression which does not account for multi-collinearity and thus during model construction one cannot gauge variable importance using p values. Thus traditional credit scorers have steered clear of interaction terms and exploratory analysis using logistic regression. Random forest variable selection overcomes that short coming and also provide a good benchmark for the estimating the flat maximum the asymptote of predictive power constraining the model. In addition, conditional inference trees were also used along with recursive partitioning to identify segments of data, as well. Conditional inferences trees identified 20 segments using age, payment date and other variables which traditional recursive partitioning could not detect and also outperformed traditional recursive partitioning. In addition to showing random forests to be power tools and models superior to logistic regression on a large credit card dataset the paper concludes by showing random forest performance against logistic regression on a widely used home equity dataset. Out of the box random forests outperform logistic regression as well as a recent optimized generalized additive neural network based logistic generalized regression model posed by Wallinga. More research is needed on an automated approach to optimally extracting predictive power from random forests and tuning logistic regression scorecards.